Joint coding method for images and videos with multiple arbitrarily shaped segments or objects

ABSTRACT

An adaptive image coding method and system are disclosed. The system accepts an input image, divides it into image segments, and assigns each segment to a wavelet transform filter from a bank of such filters for transformation. The bank preferably comprises filters adapted for different types of image content, e.g., sharp edges, slowly-varying contours, etc. Filters are preferably assigned so as to produce minimal distortion for their assigned image segments at a given target bit rate.  
     Each filter produces transform coefficients for its segment using scale and subband settings common to the entire image. The valid coefficients for each segment are then combined in a composite wavelet coefficient image, which resembles a single wavelet transform of an entire image—although different filters are used to create different portions of the coefficient image. The composite image allows joint, rate-distortion optimized coding for a segmented image. Joint coding allocates bits between the transforms of the image segments optimally, and produces an easily scaleable bitstream.

FIELD OF THE INVENTION

[0001] This invention pertains generally to methods and systems forcompression of digital images, and more particularly to coding of asegmented image using multiple wavelet transforms.

BACKGROUND OF THE INVENTION

[0002] Conventional digital images represent a visual scene using arelatively large amount of data. Visual scenes are usually digitized ina pixel grid of rows and columns, with each pixel allocated a fixednumber of bits to represent gray shade or color. For example, a typicalpersonal computer screen can display an image 1024 pixels wide, 768pixels high, with 16 bits allocated for each pixel to display color—asingle such image requires over 12.5 million bits of storage. If thissame screen were used to display digital video at 60 frames per second,the video would require a data rate of 755 million bits persecond—roughly the combined data rate of 12,000 conventional telephoneconversations. Digital image technology now extends, and will continueto be extended, into applications where data volumes such as thoseexemplified above are undesirable, and in many instances, unworkable.

[0003] Most digital images must be compressed in order to meettransmission bandwidth and/or storage requirements. Lossless imagecoders generally seek out redundancies in image data (e.g., spatial,intensity, or temporal correlation) that can be coded more efficientlywithout loss of information content. Compression gains with losslesscoders are generally modest. Lossy coders throw away part of the fullprecision image data during compression. Although many lossy imagecoders can produce images and videos compressed to only a fraction of abit per pixel, the quality of a reconstructed lossy-compressed image ata given compression rate may vary greatly from coder to coder.

[0004] Some lossy coders transform an image before compressing it. Thetransform step in a coder (hopefully) allows the coder to better rankthe significance of image information content. The transform coder thenkeeps only what it determines to be more significant transformed imageinformation, and discards the remainder. An inverse transform laterreconstructs the image from the partial transform data.

[0005] Different transforms parse image information in different ways. Adiscrete cosine transform (DCT) represents an image in terms of itssinusoidal spatial frequency. A discrete wavelet transform (DWT)represents an image using coefficients representing a combination ofspatial location and spatial frequency. Furthermore, how well a DWTparses location and frequency information on a given image depends onthe particular wavelet function employed by the DWT. For instance, theHaar wavelet function efficiently codes text and graphics regions, whilethe 9-7 tap Daubechies wavelet function performs well for coding naturalimages.

SUMMARY OF THE INVENTION

[0006] A “best” wavelet transform coder can generally be selected from aset of coders for any image, given some measurable quality criteria. Ithas now been found that this concept can be extended to subregions of animage. The present invention is directed to transform coders capable ofprocessing multiple image subregions, each with a different transformfunction. Preferably, such transform coders have the capability toprocess arbitrary-shaped image subregions. For purposes of thisdisclosure, an image subregion is synonymous with an image “segment”.

[0007] Prior subregion coders are limited by several constraints thatthe present invention seeks to overcome. First, existing subregioncoders require that each image subregion form a rectangle. Second, priorsubregion coders code each subregion separately. Third, prior subregioncoders do not lend themselves well to embedded coding techniques.

[0008] The present invention utilizes the arbitrary shape wavelettransform (ASWT), which allows an image to be divided intoarbitrarily-shaped subregions. The subregions are wavelet-transformedseparately with a “best” wavelet filter from a finite set of filters.Then, the wavelet transforms of the image segments are combined in acoherent manner prior to coding. This combination step allows the coderto “optimally” allocate bits between subregions, each having been“optimally” transformed.

[0009] In one aspect of the present invention, a method for wavelettransform coding of a segmented digital image is disclosed. The methodcomprises applying a first wavelet transform filter at a given waveletdecomposition level to a first segment of an image, thereby obtaining afirst set of transform coefficients. A second wavelet transform filteris applied at the same wavelet decomposition level to a second segmentof an image to obtain a second set of transform coefficients. The firstand second sets of transform coefficients are then merged to form acomposite wavelet coefficient image. The composite wavelet coefficientimage may then be coded with any conventional wavelet transformcoder—implicitly, the coder will jointly allocate bits to each segmentoptimally, through bit allocation on the composite wavelet coefficientimage. This method may be extended to include additional wavelettransform filters and finer image segmentation.

[0010] Preferably, the wavelet transform set utilizes the arbitraryshape wavelet transform (ASWT), which can process image segments inalmost any shape. The present invention also allows joint bit allocationwith embedded coding.

[0011] The present invention performs two types of image segmentation.In the first type, segmentation decisions are input to the processexternally. In the second type, segmentation is coupled with filterassignment, such that segmentation in at least some sense tracks optimalspatial filter assignment.

[0012] In another aspect of the invention, a digital image coder isdisclosed. This system comprises an image segmentor, a wavelet filterbank, a composite wavelet coefficient mapper, and, preferably, atransform coder. The image segmentor parses segments of an input imageto filters from the wavelet filter bank. Each wavelet filter computeswavelet transform coefficients for its image segment. The compositewavelet coefficient mapper gathers the wavelet coefficients produced byeach wavelet filter into a composite coefficient image, arranging themas if they were produced from a single wavelet transform. Finally, thetransform coder codes the composite coefficient image.

BRIEF DESCRIPTION OF THE DRAWING

[0013] The invention may be best understood by reading the disclosurewith reference to the following figures:

[0014]FIG. 1, which illustrates a block diagram of an image coderaccording to an embodiment of the present invention;

[0015]FIG. 2, which illustrates a digital image at several steps ofimage coding according to the present invention;

[0016]FIG. 3, which plots distortion as a function of bits coded for ahypothetical two-region image;

[0017]FIG. 4, which illustrates a filter selector useful with thepresent invention;

[0018]FIG. 5, which contains a flow chart for a joint segmentor/filterselector useful with the present invention;

[0019]FIG. 6, which illustrates a quadtree-segmented image prior to leafnode merging, and its corresponding quadtree structure; and

[0020]FIG. 7, which shows the same quadtree-segmented image after leafnode merging, and its corresponding quadtree structure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] The block diagram of FIG. 1 illustrates the major functionalblocks comprehended by the present invention. A segmentor/filterselector 22 accepts a digital image 24 and parses segments of image 24to wavelet transform filters (e.g. 28, 30, 32) in a filter bank 26.Using a common wavelet decomposition level setting, each filter infilter bank 26 performs a wavelet transform on segments of image 24assigned to it. The wavelet coefficients produced by filters 28, 30, 32are fed to joint embedded coder 34, where a wavelet coefficient mappercombines the wavelet coefficients into a composite wavelet coefficientimage. Embedded coder 34 outputs a bitstream based on the compositewavelet coefficient image.

[0022]FIG. 2 presents an image proceeding through the coding processpictorially. Digital image 24 is segmented, producing background segment36 and coast guard cutter segment 38. Background segment 36 is fed to afirst arbitrary shape wavelet transform filter 28, which outputs abackground wavelet coefficient map 40. Cutter segment 38 is fed to asecond arbitrary shape wavelet transform filter 30, which outputs acutter wavelet coefficient map 42. Coefficient maps 40 and 42 registertransform coefficients using a common scale and subband structurecorresponding to the original image format (e.g., rectangular as in FIG.2). This allows a coefficient mapper to merge coefficient maps 40 and 42into a composite coefficient image 46. Composite coefficient image 46has the scale and subband structure of a conventional rectangular singlewavelet transform of digital image 24; thus coefficient image 46 may becoded directly using rectangular wavelet coefficient image coders, suchas rate-distortion embedded coder 48.

[0023] The coding process illustrated by FIGS. 1 and 2 activatesdifferent wavelet transforms for arbitrarily-shaped image segments 36and 38. Proper filter selection for each segment can reduce the entropyof the wavelet coefficients as compared to the entropy of the waveletcoefficients where the entire image 24 is transformed by a singlefilter. An entropy-estimation filter selection method, described in aseparate section below, enhances performance of the invention.

[0024] A distinct separate advantage of the invention is the ability tojointly code the transform coefficients of segments 36 and 38.Coefficient maps 40 and 42 could theoretically be separately, andoptimally, coded, as long as the decoder knows the segmentationboundaries of image 24. For compression, a target bit rate could besupplied to two coders, one for each coefficient map 40 and 42. However,if the rate-distortion curves for maps 40 and 42 differ, as theygenerally will, coding each map at a common target bit rate will producean inferior result compared to joint coding.

[0025]FIG. 3 graphs rate-distortion curves for two hypothetical regions,each originally represented by 32 bits. Region 1 is represented by curve50, and region 2 is represented by curve 52. Each region maytheoretically be coded in a rate-distortion optimal manner that follows,respectively, curves 50 and 52. Rate distortion curve 54 represents thecombined 64-bit output bit sequence of the two coders, assuming the twocoders alternate in outputting bits. Even though each coder operatesoptimally for its region, the combined output of the two coders issuboptimal for virtually all target bit rates if available bits areevenly divided between the two regions.

[0026] The present invention allows joint coding because a consistentscale and subband coefficient structure is produced by each filter inwavelet filter bank 26. This attribute allows a coefficient mapper tomerge wavelet coefficients for two or more regions and perform a jointcoding on the merged coefficients. For instance, curve 56 of FIG. 3represents rate distortion after merging of the regions represented bycurves 50 and 52. A joint coder can operate on the merged coefficientsin a rate-distortion optimal manner that will follow curve 56, beatingthe performance of two region coders operating at the same aggregate bitrate. Although two coders may beat curve 54 at a given target bit rateby allocating bits non-uniformly, the joint coder solves the bitallocation problem implicitly.

[0027] Segmentation and Filter Selection

[0028] Two general segmentor/filter selector embodiments are envisionedby the present invention. The first embodiment segments an input imageaccording to supplied instructions, and selects a wavelet transformfilter for each identified segment. FIG. 2 illustrates the operation ofthis embodiment, where a segmentation mask of the coast guard cutter wassupplied to the segmentor/filter selector. The second embodiment acceptsan input image and performs segmentation and filter selection jointly;i.e., an initial segmentation is refined using filter selection results.

[0029] A flow chart for an estimated entropy based filter selector 72useful with either embodiment appears in FIG. 4. Filter selector 72chooses a wavelet transform filter from N available filters for eachimage segmentj using entropy estimator 58. For a given filter i andsegment j, estimator 58 determines an estimated entropy Ê_(ij). Decisionblock 66 compares values of Ê_(ij) for each filter, and signals block 68to associate the current filter i with the current segment j if Ê_(ij)is the lowest entropy value yet received for segment j. After loopingthrough N filters, segment j will be associated with the filter iproducing the lowest estimated entropy Ê_(ij). This association is thenused to parse image segments to the wavelet transform filters.

[0030] The present invention recognizes that for image compressionapplications, filter selection results are rate-sensitive. FIG. 4 showsan entropy estimator 58 developed for use with the present inventionthat incorporates rate sensitivity into filter selection. In block 60,image segment j is wavelet decomposed to a preset depth using eachfilter from the candidate set of N filters. Block 62 uniformly quantizeseach wavelet decomposition using a fixed rate-matched quantizer stepsize, e.g., based on an average target bit rate or average distortiondesired. Block 64 then calculates the first order entropy of thequantized coefficients to produce the estimated entropy value Ê_(ij).

[0031] In many systems using the present invention, the purpose of imagesegmentation is to allow more efficient image coding. The jointsegmentation/filter selection flowchart of FIG. 5 illustrates onepreferred method of segmentation/filter selection. Block 70 performs aninitial fixed segmentation of an input image—for simplicity, quadtreesegmentation may be used at this step. Block 72 performs filterselection, e.g., as shown in FIG. 4, on each segment identified by block70. Neighboring segments associated with the same filter are then mergedinto larger segments in block 74. Block 76 then parses segments to theirassociated filters for decomposition.

[0032] Referring now to FIGS. 6 and 7, an illustration of jointsegmentor/filter selector operation is presented. In FIG. 6, block 80represents an input image that has been initially segmented into 16equally-sized subregions (numbered 0000 through 1111 binary) by aquadtree segmentor. Quadtree 82 shows the relationship of the segmentsin the quadtree mapping of segmented image 80.

[0033] Wavelet filters A, B, and C are applied to each subregion0000-1111, and a best filter is chosen for each subregion based onentropy estimation. A sample filter assignment is shown on 80 by theplacement of “A”, “B”, or “C” on the subregion to show the assignedfilter. The leaf nodes on quadtree 82 are also labeled with the selectedfilter for that node.

[0034] After all leaf subregions have been assigned a filter type A, B,or C, the tree is merged from the bottom up recursively. If the childrenof a parent node are assigned the same type of filter, the children willbe merged and the same type of filter will be assigned to the parentnode. FIG. 7 shows the original segmentation and filter assignment ofFIG. 6 after this merging process. Of the original leaf segments ofquadtree 82, only segments 1000 and 1001 remain in quadtree 86. Allother original leaf segments have been merged up one or two nodes due tocommon filter assignment. Segmentation map 84 shows the final filterassignment for this example.

[0035] Filter re-selection is not performed at intermediate nodes aftermerging, as it is believed that re-association is highly unlikely. Thespatial quadtree structure itself is not critical to the rate-distortionoptimization process—merging merely facilitates the coding of thesegmentation map. The merging also reduces the number of boundaries inthe segmentation map, reducing the potential for boundary artifactswhich may occur at low bit rates.

[0036] In principle, the quadtree segmentation result will depend on thefinal target bit rate selected for entropy measurement. Using differentsegmentation maps for different intermediate bit rates makes itextremely difficult, if not impossible, to generate a (preferablycontinuously) scalable bit stream. Thus a single segmentation map ispreferred to allow freedom in scalability. Given the choice between asegmentation map generated using a higher target bit rate and onegenerated at a lower target bit rate, the map generated with a highertarget bit rate is preferred. The high target bit rate map is believedto more accurately reflect the characteristics of each region. It isalso believed that perceptual quality of a low-bit rate image may bedegraded if the segmentation map is generated at too low of a target bitrate.

[0037] Arbitrary-Shape Wavelet Transform

[0038] The concepts disclosed herein can be utilized with purelyrectangular segmentations of an image (such as segmentation of image 80in FIG. 6). It is preferred, however, that the wavelet transform filtersnot constrain the segmentation result in this manner. This can reduceboundary artifacts that may occur if an image must be processed as a setof rectangles. This also enables a system to perform coding on complexsegmentations such as the coast guard cutter of FIG. 2.

[0039] In order to allow wavelet transforms of non-rectangular segments,filter bank 26 of FIG. 1 preferably utilizes arbitrary shape wavelettransform (ASWT) filters. The ASWT is described in detail in U.S. patentapplication Ser. No. 09/110,979, entitled “Arbitrary Shape WaveletTransform with Phase Alignment” and filed Jul. 7, 1998, by J. Li and S.Lei, which is incorporated herein by reference: Briefly, the ASWTsymmetrically extends signal pixels at segment boundaries by replicatingthem into adjacent pixels (not part of the segment) in reverse order,and then wavelet transforming the extended pixel image. Waveletcoefficients obtained from the transform that correspond to the originalsegment pixels are retained.

[0040] In order to facilitate coefficient mapping, each segment shouldbe extended to a common size during the ASWT process (preferably theoriginal image size). The wavelet decomposition level for eachsegment-ASWT combination is also set to the same value such that theASWT-decomposed segments exhibit a common scale and subband structure.

[0041] Coefficient Mapping

[0042] Coefficient mapping combines the coefficients obtained with eachsegment-ASWT combination into a composite wavelet coefficient image. Thecommon image size of all ASWT coefficient maps infers that coefficientscan be copied address-by-address directly into the composite waveletcoefficient image. One ASWT coefficient map may even serve as thecomposite image (preferably the one representing the largest imagesegment), with other coefficients copied into its invalid locations.

[0043] The mapper must be able to locate the valid pixels in eachcoefficient map. This may be accomplished by “tagging” invalid pixelswith an unused data value, or using a coefficient segment map tofacilitate copying. If the original image segments are disjoint, validpixels should not exist at the same location for two ASWT coefficientmaps.

[0044] The mapping process is shown pictorially in FIG. 2. Coast guardcutter segment 38 is segmented from its background segment 36. Thebackground segment 36 maps, using a first ASWT 28, into a firstcoefficient map 40. Coast guard cutter segment 38 maps, using a secondASWT 30 into a second coefficient map 42. The common scale and subbandstructure of maps 40 and 42, as well as the dark, mutually disjointinvalid regions of each map, are clearly visible in FIG. 2. The mappingprocess copies background and coast guard cutter transform coefficientsinto their properly registered locations in composite waveletcoefficient image 46.

[0045] Note that although composite image 46 represents scale andsubband structure consistently, the composite image cannot be directlyprocessed using an inverse wavelet transform to obtain the originalimage. A de-mapping, signal boundary extension, and inverse wavelettransform must be performed on each segment separately to recover theoriginal image information.

[0046] Transform Coders

[0047] Wavelet transform images are commonly coded for transmission orstorage. An advantage of the present invention is the presentation of asegmented, wavelet transformed image in a standard (e.g. rectangular)image format that is compatible with known transform coders. Thisadvantage allows a segmented image to be jointly coded usingwell-developed optimal techniques for rectangular wavelet transformimage coding.

[0048] One preferred coding technique that may be employed with thepresent invention is rate-distortion optimized embedded (RDE) coding.This technique is disclosed in U.S. patent application Ser. No.09/016,571, “An Embedded Image Coder with Rate-Distortion Optimization”,filed Jan. 30, 1998, by J. Li and S. Lei, which is incorporated hereinby reference.

[0049] RDE is a rate-distortion optimized coding technique. This coderreorders the bits of a wavelet transform image into amost-important-bits-first bitstream, i.e. allocating the availablecoding bits first to image bits having the steepest rate-distortionslope. The rate-distortion slope for uncoded bits is predicted usingonly previously-coded bits—this allows both the coder and decoder tooperate in lockstep without image position information from the coder.

[0050] The RDE coder processes a composite wavelet coefficient imageproduced using the present invention in similar fashion. Bit allocationamong different image segments is thus achieved implicitly, as the RDEcoder considers the rate distortion in all image segments jointly. Theeffect of joint rate distortion consideration is evident in FIG. 3. Ajoint coder operating along curve 56 out performs two interleavedoptimal coders operating along curve 54 because the joint coderimplicitly recognizes that one of two regions (curve 52) contains manymore meaningful bits than the other region (curve 50). Curve 56 reordersbit transmission accordingly.

[0051] Test Results

[0052] Experimental results were obtained using a candidate set of threeprototype wavelet filters in bank 26. The three filters chosen were theHaar filter and the 9-3 tap and 9-7 tap Daubechies biorthogonal filters.The Haar filter is the only known symmetric, orthogonal wavelet filter,and is a good choice for text and graphics image regions. The 9-7 tapDaubechies filter is the most popular filter used in the literature forcoding of natural images, and generally has very good rate-distortionperformance. The 9-3 tap Daubechies filter, with shorter filter lengththan the 9-7 tap filter, is expected to have less ringing artifactsaround sharp edges at low bit rates. A five-level wavelet decompositionwith symmetric boundary extension was used throughout these experiments.

[0053] For each test image, the segmentation map was generated using afilter selection target bit rate of 4 bits per pixel. This singlesegmentation map was then used for the adaptive wavelet transform at alltested bit rates. The depth of the initial spatial quadtree was adaptedto the size of the test image; quadtree segmentation was stopped whenthe shorter side of each leaf block was between 32 and 64 pixels inlength. To reduce the potential segment boundary effect at lower bitrates, a slightly modified version of the simple deblocking algorithmpresented in S. Kim et al., “Results of Core Experiment N1”, ISO/IECJTC1/Sc29/WG11 MPEG97/M2099, Apr. 1997, was used.

[0054] Table 1 summarizes the rate-distortion performance of thisexperiment at several target bit rates. The adaptive segmentation andtransform method with three candidate filters is compared to each of thethree filters operating separately on an entire image, with each usingthe same backend RDE coding. TABLE 1 Coding PSNR (dB) Bit Rate (bits perpixel) Image Filter 0.125 0.25 0.5 0.75 1 2 Cmpnd1 Haar 22.37 28.3538.90 45.56 50.82 90.46 (512 × 768) 9-3 tap 21.77 26.50 31.98 34.9236.31 37.28 9-7 tap 22.46 26.69 32.04 35.07 36.58 38.14 Adaptive 23.9930.26 40.11 46.73 51.80 89.76 Target Haar 19.31 23.15 30.55 36.54 41.4555.12 (512 × 512) 9-3 tap 21.56 26.16 32.16 37.23 41.12 48.26 9-7tap22.11 26.54 33.54 37.89 42.26 49.15 Adaptive 22.04 26.8 34.14 39.3844.78 63.30 Container Haar 25.21 28.11 31.73 34.23 36.31 42.33 (352 ×288) 9-3 tap 25.49 28.38 32.05 34.74 36.86 43.44 9-7 tap 25.84 28.5632.16 34.88 37.11 43.59 Adaptive 25.46 28.41 32.13 34.88 37.00 43.49

[0055] All three test images were grayscale images. The image “cmpnd1”contains both text and natural image regions. The image “target”comprises graphics and charts. And the image “container” is a scenecontaining both natural and man-made structure.

[0056] For relatively higher bit rates, PSNR improvements of up to 2 dBfor the adaptive method versus the best non-adaptive method weremeasured for “cmpnd1” and “target” images. At lower bit rates, PSNR wasnot necessarily improved by the adaptive method on all test images. Thisis believed to be due at least in part to the filterselection/segmentation process based on a fixed, relatively high bitrate. Despite this, visual quality for the adaptively-coded images wassignificantly improved throughout the test set, particularly at mediumto lower bit rates. The proposed invention appears particularlyapplicable to compression of images exhibiting space-varyingcharacteristics, such as compound images (e.g. graphics and naturalscenes) or other images exhibiting sharp edge regions and smoothregions.

[0057] Although the preferred embodiments have been described withreference to a particular process flow, numerous variations of theseembodiments fall within the scope of the present invention. Theassociation of image segments with particular candidate wavelet filters,for instance, may use different statistics or methods from thosedisclosed herein without departing from the invention. It is notnecessary that each filter in the filter bank be used on a segment ofeach input image. Likewise, several disjoint segments may be assignedthe same filter. Although segments are coded jointly, bit allocationpriorities may be segment-weighted, e.g., by scaling the transformcoefficients for each segment before RDE or other coding. Using acoefficient segmentation map, separate bitstreams may also be createdfor each segment at the output of the RDE or other coder, after bitallocation and coding.

[0058] Other modifications to the disclosed embodiments will be obviousto those of ordinary skill in the art upon reading this disclosure, andare intended to fall within the scope of the invention as claimed.

What is claimed is:
 1. A method for wavelet transform coding a digitalimage, said method comprising: selecting a wavelet decomposition level;applying a first wavelet transform filter at said wavelet decompositionlevel to a first segment of a digital image, thereby obtaining a firstset of transform coefficients; applying a second wavelet transformfilter at said wavelet decomposition level to a second segment of saiddigital image, thereby obtaining a second set of transform coefficients;and merging said first and second sets of transform coefficients into acomposite wavelet coefficient image.
 2. The method of claim 1, whereinsaid merging step comprises placing coefficients from said first andsecond sets of transform coefficients into spatial locations on saidcomposite wavelet coefficient image consistent with the waveletdecomposed spatial locations of their respective segment transformcoefficients for the entire digital image.
 3. The method of claim 1,further comprising the step of compressing said composite waveletcoefficient image using a lossless compressor.
 4. The method of claim 1,further comprising the step of compressing said composite waveletcoefficient image using a lossy compressor.
 5. The method of claim 4,wherein said lossy compressor comprises an embedded coder operating at atarget bit rate lower than the original bit rate of the digital image.6. The method of claim 1, further comprising the step of rearranging thebits of said composite wavelet coefficient image for transmission orstorage using a rate-distortion optimized embedded coder.
 7. The methodof claim 1, wherein said first and second wavelet transform filtersaccept arbitrary-shaped image segments.
 8. The method of claim 1,further comprising the step of selecting said first wavelet transformfilter from among a set of candidate wavelet transform filters forapplication to said first segment of said digital image based onmeasured characteristics of said first segment.
 9. The method of claim1, further comprising the step of segmenting said digital image intosaid first and second segments, said segmenting step comprising thesubsteps of: initially segmenting said digital image into a plurality ofleaf segments; applying each filter from a set of candidate wavelettransform filters to each leaf segment to produce a set of waveletcoefficients for each filter-leaf segment pair; quantizing each set ofwavelet coefficients using a preset step size; calculating an entropyvalue for each set of wavelet coefficients; for each leaf segment,identifying the minimum entropy value from among all entropy valuescorresponding to that leaf segment, and assigning the filter from thefilter-leaf segment pair corresponding to the minimum entropy value tothat leaf segment; and merging neighboring leaf segments into largerleaf segments where neighboring leaf segments are assigned a commonfilter from said set of candidate wavelet transform filters.
 10. Themethod of claim 9, wherein said initially segmenting step comprisesquadtree segmenting said digital image into a plurality of leaf segmentsand wherein said merging step comprises recursively merging leafsegments into a larger leaf segment where all leaf segments sharing acommon parent node on said quadtree are assigned a common filter fromsaid set of candidate wavelet transform filters.
 11. A method forwavelet transform coding a digital image, said method comprising:accepting a digital image and a segmentation map of said digital image,said segmentation map grouping pixels of said digital image intomultiple contiguous segments; associating an arbitrary shape wavelettransform filter from a set of candidate filters with each of saidcontiguous segments, wherein at least two of said contiguous segmentsare associated with different filters from said set of candidatefilters; wavelet transforming each contiguous segment to a commondecomposition level using the segment's associated arbitrary shapewavelet transform filter, thereby obtaining a set of transformcoefficients for each contiguous segment; and merging each set oftransform coefficients into a composite wavelet coefficient image. 12.The method of claim 11, wherein said merging step comprises placingcoefficients from each set of transform coefficients into spatiallocations on said composite wavelet coefficient image consistent withthe wavelet decomposed spatial locations of their respective segmenttransform coefficients for the entire digital image.
 13. The method ofclaim 11, wherein said associating step comprises estimating the entropyof coding each of said contiguous segments with each filter from saidset of candidate filters, and selecting, for each of said contiguoussegments, a filter from said set of candidate filters that produces theminimum estimated entropy for that segment.
 14. A digital image coder,said coder comprising: a segmentor for parsing a digital image intomultiple image segments; a bank of wavelet transform filters, each ofsaid filters producing a set of wavelet transform coefficients for oneor more image segments associated with that filter by said segmentor;and a composite wavelet coefficient mapper that combines sets of wavelettransform coefficients produced by said bank of wavelet transformfilters for said digital image into a common wavelet coefficient image.15. The image coder of claim 14, wherein said bank of wavelet transformfilters is comprised of arbitrary-shaped wavelet transform filters. 16.The image coder of claim 14, further comprising an embedded coder thatoperates on a common wavelet coefficient image produced by saidcomposite wavelet coefficient mapper.
 17. The image coder of claim 16,wherein said embedded coder selects an output bit order for a commonwavelet coefficient image using a rate-distortion optimization criteria.18. The image coder of claim 14, wherein said segmentor parses a digitalimages according to segment boundary information supplied to said coder.19. The image coder of claim 14, wherein said segmnentor comprises afilter selector, said filter selector pairing each segment of a digitalimage with a filter from said bank of arbitrary-shape wavelet transformfilters based on measured characteristics of that segment.
 20. The imagecoder of claim 19, wherein said filter selector comprises an entropyestimator that estimates a transform entropy value for each combinationof image segment and filter from said bank of arbitrary-shape wavelettransform filters, and wherein said filter selector pairs each segmentof a digital image with the filter from said bank of arbitrary-shapewavelet transform filters producing the minimum estimated entropy forthat segment.
 21. A digital image coder, said coder comprising: asegmentor for parsing a digital image into multiple arbitrarily-shapedimage segments; a bank of arbitrary-shape wavelet transform filters,each of said filters producing a set of wavelet transform coefficientsfor one or more image segments associated with that filter by saidsegmentor; a composite wavelet coefficient mapper that combines sets ofwavelet transform coefficients produced by said bank of wavelettransform filters for said digital image into a common waveletcoefficient image; and a rate-distortion optimizing embedded coder thatoperates on a common wavelet coefficient image produced by saidcomposite wavelet coefficient mapper to produce a scaleable outputbitstream.